Student Ratings & Instructor Teaching Through Analytics
Explore the significance of student ratings in evaluating instructor teaching using Decision Trees and Anomaly Detection. Discover the variables used and application of analytics in institutional research, including student segmentation and course planning.
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Presentation Transcript
What What Matters in Student Rating Matters in Student Rating of Instructor Teaching (SRI)? of Instructor Teaching (SRI)? Decision Trees (CHAID)
Competitive Competitive Positioning Cluster Analysis (K-Means) Positioning
Variables Used in the Analysis 1. Books per Faculty 2. Articles per Faculty 3. Citations per Faculty 4. Awards per Faculty 5. Grant Dollars per Faculty (federal) 6. Grants per Faculty 7. Conference Proceedings per Faculty
Anomaly Detection Anomaly detection models are used to identify outliers, or unusual cases, in the data. Unlike other modeling methods that store rules about unusual cases, anomaly detection models store information on what normal behavior looks like. Anomaly detection is an exploratory method designed for quick detection of unusual cases or records that should be candidates for further analysis. For example, the algorithm might lump records into three distinct clusters and flag those that fall far from the center of any one cluster. Source: SPSS, 2014
Application Application of Analytics in Institutional Research of Analytics in Institutional Research Categorize your students Classification/Segmentation Cafeteria meal planning Student housing planning Identify high risk students Estimate/predict alumni contributions Predict new student application rate Predict students retention/Alumni donations Neural Nets/Regression Course planning Academic scheduling Identify student preferences for clubs and social organizations Group similar students Clustering Identify courses that are taken together Association Faculty teaching load estimation Course planning Academic scheduling Predict alumni donations Predict potential demand for library resources Find patterns and trends over time Sequence Source: Thulasi Kumar, 2004
